Sensor Fault Detection and Diagnostics of Nonlinear Systems with Application to Paper Machines

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This paper presents an efficient real-time method of sensor fault detection, isolation and estimation for a class of MIMO nonlinear time-varying stochastic systems. The sensor faults may be a jump or slow drift type. The presented approach is based on the combination of the separate-bias estimation algorithm to randomly time-varying bias of nonlinear systems proposed by the authors and the modified Bayes' decision algorithm. The proposed technique is used to design a sensor fault diagnostics system for the headbox of an actual paper machine. Actual headbox data have demonstrated the effectiveness of the proposed method